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基于局部多项式的右删失部分线性回归的惩罚与收缩策略

Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression.

作者信息

Ahmed Syed Ejaz, Aydın Dursun, Yılmaz Ersin

机构信息

Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada.

Department of Statistics, Mugla Sıtkı Kocman University, 48000 Mugla, Turkey.

出版信息

Entropy (Basel). 2022 Dec 15;24(12):1833. doi: 10.3390/e24121833.

DOI:10.3390/e24121833
PMID:36554238
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9778259/
Abstract

This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model. In this context, the methods used to obtain the estimators are ridge, lasso, adaptive lasso, SCAD, MCP, and elasticnet penalty functions. The most important contribution that distinguishes this article from its peers is that it uses the local polynomial method as a smoothing method. The theoretical estimation procedures for the obtained estimators are explained. In addition, a simulation study is performed to see the behavior of the estimators and make a detailed comparison, and hepatocellular carcinoma data are estimated as a real data example. As a result of the study, the estimators based on adaptive lasso and SCAD were more resistant to censorship and outperformed the other four estimators.

摘要

本研究旨在基于六种不同的惩罚和收缩策略,提出修正的半参数估计量,用于估计右删失半参数回归模型。在此背景下,用于获得估计量的方法有岭回归、套索回归、自适应套索回归、平滑截断绝对偏差(SCAD)、最小角回归(MCP)和弹性网惩罚函数。本文与同行文章最显著的区别在于,它使用局部多项式方法作为平滑方法。文中解释了所获估计量的理论估计程序。此外,进行了一项模拟研究,以观察估计量的表现并进行详细比较,并且将肝细胞癌数据作为实际数据示例进行估计。研究结果表明,基于自适应套索回归和SCAD的估计量对删失更具抗性,并且优于其他四个估计量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a26/9778259/0652267f3805/entropy-24-01833-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a26/9778259/4e93b1560c0b/entropy-24-01833-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a26/9778259/04ffd086f702/entropy-24-01833-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a26/9778259/8bb52e3c196e/entropy-24-01833-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a26/9778259/0652267f3805/entropy-24-01833-g014.jpg

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本文引用的文献

1
Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator.基于修正半参数A样条估计器的右删失时间序列建模
Entropy (Basel). 2021 Nov 27;23(12):1586. doi: 10.3390/e23121586.
2
CXCL17 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcinoma.CXCL17表达预示肝细胞癌预后不良,并与不良免疫浸润相关。
PLoS One. 2014 Oct 10;9(10):e110064. doi: 10.1371/journal.pone.0110064. eCollection 2014.
3
Quantification of metamorphopsia in a macular hole patient using M-CHARTS.
使用M图表对黄斑裂孔患者的视物变形进行量化。
Acta Ophthalmol Scand. 2007 Feb;85(1):55-9. doi: 10.1111/j.1600-0420.2006.00729.x.
4
Censored partial regression.删失部分回归
Biostatistics. 2003 Jan;4(1):109-21. doi: 10.1093/biostatistics/4.1.109.